Discriminant quaternion local binary pattern embedding for person re-identification through prototype formation and color categorization

Chahla, C.; Snoussi, Hichem; Abdallah, Fahed; Dornaika, Fadi

doi:10.1016/j.engappai.2016.11.004

Cited by 15 publications

(3 citation statements)

References 25 publications

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“…The local binary pattern (LBP) extracts the pixels' pattern by labeling it with a decimal number, called LBPs [33,34]. LBP method is achieved by thresholding the neighborhood of each pixel with a binary number [35].…”

Section: Texture Based Featurementioning

confidence: 99%

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Wazirali¹,

Ahmed²

2022

Computer Systems Science and Engineering

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The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required feature is the point of concern. This paper has proposed adopted multiple features and emphasis on the periocular region. In the proposed approach, combination of local binary pattern (LBP), color histogram and features in frequency domain have been used with deep learning algorithms for classification. Hence, we extract three types of features for the classification of periocular regions for biometric. The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel. In order to extract the frequency domain features, the wavelet transformation is obtained. By learning from these features, a convolutional neural network (CNN) becomes able to discriminate the features and can provide better recognition results. The proposed approach achieved the highest accuracy rates with the lowest false person identification.

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Section: Texture Based Featurementioning

confidence: 99%

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Wazirali¹,

Ahmed²

2022

Computer Systems Science and Engineering

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“…Under this representation, the dimension of QLBP is equal to the dimension of a grayscale LBP. QLBP was applied for person re-identification problems by Lan and Chahla in [ 45 , 46 ] and was then extended in [ 47 ] for color image classification.…”

Section: Color Lbp Histogramsmentioning

confidence: 99%

Combination of LBP Bin and Histogram Selections for Color Texture Classification

et al. 2020

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LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

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“…Research in people behavior analysis has been thoroughly focused on person re-id during the last decade, which has seen the exploitation of many paradigms and approaches of pattern recognition [ 1 , 2 , 3 ]. In challenging situations, algorithms need to be robust to be able to deal with issues such as widely-varying camera viewpoints and orientations, rapid changes in the appearance of clothing, occlusions, varying poses and various lighting conditions [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection

Paolanti

Romeo

Liciotti

et al. 2018

Sensors

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Person re-identification is an important topic in retail, scene monitoring, human-computer interaction, people counting, ambient assisted living and many other application fields. A dataset for person re-identification TVPR (Top View Person Re-Identification) based on a number of significant features derived from both depth and color images has been previously built. This dataset uses an RGB-D camera in a top-view configuration to extract anthropometric features for the recognition of people in view of the camera, reducing the problem of occlusions while being privacy preserving. In this paper, we introduce a machine learning method for person re-identification using the TVPR dataset. In particular, we propose the combination of multiple k-nearest neighbor classifiers based on different distance functions and feature subsets derived from depth and color images. Moreover, the neighborhood component feature selection is used to learn the depth features’ weighting vector by minimizing the leave-one-out regularized training error. The classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Experimental results show that the proposed methodology outperforms standard supervised classifiers widely used for the re-identification task. This improvement encourages the application of this approach in the retail context in order to improve retail analytics, customer service and shopping space management.

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Discriminant quaternion local binary pattern embedding for person re-identification through prototype formation and color categorization

Cited by 15 publications

References 25 publications

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

Combination of LBP Bin and Histogram Selections for Color Texture Classification

Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection

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